Analysis of confocal microendoscope images for automatic detection of ovarian cancer

The fluorescence confocal microendoscope (CM) is a new type of instrument for imaging the surface of the human ovary and has diagnostic implications for the early detection of ovarian cancer. The purpose of this study was to develop an automated system to facilitate the identification of ovarian cancer from digital images captured with the CM system. We modeled the cellular distribution present in the images as texture and extracted features based on first-order statistics, spatial gray-level dependence matrices, and spatial-frequency content. We believe this is the first time that automated texture analysis has been used to detect ovarian cancer in CM images. Experiments were conducted to select the best features for classification and to compare the performance of machine classifiers. The results show that it is possible to automatically identify ovarian cancer based on texture features extracted from CM images and that the machine performance is superior to that of the human observer.